In the previous recipe, we showed how to manually code the Longstaff-Schwartz algorithm. However, we can also use already existing frameworks for valuation of derivatives. One of the most popular ones is QuantLib. It is an open source C++ library that provides tools for the valuation of financial instruments. By using **Simplified Wrapper and Interface Generator** (**SWIG**), it is possible to use QuantLib from Python (and some other programming languages, such as R or Julia). In this recipe, we show how to price the same American put option that we priced in the *Pricing American options with Least squares Monte Carlo* recipe, but the library itself has many more interesting features to explore.

#### Python for Finance Cookbook

##### By :

#### Python for Finance Cookbook

##### By:

#### Overview of this book

Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries.
In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks.
By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach.

Table of Contents (12 chapters)

Preface

Financial Data and Preprocessing

Free Chapter

Technical Analysis in Python

Time Series Modeling

Multi-Factor Models

Modeling Volatility with GARCH Class Models

Monte Carlo Simulations in Finance

Asset Allocation in Python

Identifying Credit Default with Machine Learning

Advanced Machine Learning Models in Finance

Deep Learning in Finance

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